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Dimensionality reduction with adaptive graph

Content Provider	Springer Nature Link
Author	Qiao, Lishan Zhang, Limei Chen, Songcan
Copyright Year	2013
Abstract	Graph-based dimensionality reduction (DR) methods have been applied successfully in many practical problems, such as face recognition, where graphs play a crucial role in modeling the data distribution or structure. However, the ideal graph is, in practice, difficult to discover. Usually, one needs to construct graph empirically according to various motivations, priors, or assumptions; this is independent of the subsequent DR mapping calculation. Different from the previous works, in this paper, we attempt to learn a graph closely linked with the DR process, and propose an algorithm called dimensionality reduction with adaptive graph (DRAG), whose idea is to, during seeking projection matrix, simultaneously learn a graph in the neighborhood of a prespecified one. Moreover, the pre-specified graph is treated as a noisy observation of the ideal one, and the square Frobenius divergence is used to measure their difference in the objective function. As a result, we achieve an elegant graph update formula which naturally fuses the original and transformed data information. In particular, the optimal graph is shown to be a weighted sum of the pre-defined graph in the original space and a new graph depending on transformed space. Empirical results on several face datasets demonstrate the effectiveness of the proposed algorithm.
Starting Page	745
Ending Page	753
Page Count	9
File Format	PDF
ISSN	20952228
Journal	Frontiers of Computer Science in China
Volume Number	7
Issue Number	5
e-ISSN	20952236
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-08-10
Publisher Institution	Chinese Universities
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Dimensionality reduction graph construction face recognition Computer Science
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computer Science

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